Synopses & Reviews

Publisher Comments:

The field of computer vision has developed significantly over the last several years and the application areas span a broad range from medicine to robotics and surveillance. Unifying Perspectives in Computational and Robot Vision provides an overview of the latest research surrounding vision algorithms and their use and evaluation with robotic applications. In robotics, computer vision is mainly seen as another sensory modality and new algorithms are commonly developed for different applications. In the computer vision community, there have been more requirements for benchmarking and comparison of methods but very few are still applied in realistic settings and in real-time which is one of the strongest requirements in robotic applications. Unifying Perspectives in Computational and Robot Vision brings the communities of robot and computer vision to a joint appreciation of the value of systems, where there is a need to consider all aspects from perception to action generation. Based on a workshop "From Features to Actions: Unifying Perspectives in Computational and Robot Vision" that was organized by the editors at the IEEE International Conference on Robotics and Automation (Rome, April 2007), Unifying Perspectives in Computational and Robot Vision will be a valuable resource for readers.

Synopsis:

Assembled in this volume is a collection of some of the state-of-the-art methods that are using computer vision and machine learning techniques as applied in robotic applications. Currently there is a gap between research conducted in the computer vision and robotics communities. This volume discusses contrasting viewpoints of computer vision vs. robotics, and provides current and future challenges discussed from a research perspective.

Synopsis:

Currently there is a gap between the research conducted in computer vision and robotics communities. There are many characteristics in common in computer vision research and vision research in robotics. Despite having these common interests, however, pure computer vision has seen significant theoretical and methodological advances during the last decade which many of the robotics researchers are not fully aware of. On the other hand, the manipulation and control capabilities of robots as well as the range of application areas have developed greatly. In robotics, vision can not be considered an isolated component, but it is instead a part of a system resulting in an action. Thus, in robotics the vision research must include consideration of the control of the system, in other words, the entire perception-action loop. This requires that a holistic system approach is useful and could provide significant advances in this application domain. Assembled here is a collection of some of the state of the art methods that are using computer vision and machine learning techniques and apply them in robotic applications.

Artificial Intelligence (incl. Robotics) <P>Demonstrates state of the art in applying computer vision in robotics</P> <P>Discusses contrasting viewpoints of computer vision vs robotics</P> <P>Provides current and future challenges discussed from research

"Synopsis"
by Springer,
Assembled in this volume is a collection of some of the state-of-the-art methods that are using computer vision and machine learning techniques as applied in robotic applications. Currently there is a gap between research conducted in the computer vision and robotics communities. This volume discusses contrasting viewpoints of computer vision vs. robotics, and provides current and future challenges discussed from a research perspective.

"Synopsis"
by Ingram,
Currently there is a gap between the research conducted in computer vision and robotics communities. There are many characteristics in common in computer vision research and vision research in robotics. Despite having these common interests, however, pure computer vision has seen significant theoretical and methodological advances during the last decade which many of the robotics researchers are not fully aware of. On the other hand, the manipulation and control capabilities of robots as well as the range of application areas have developed greatly. In robotics, vision can not be considered an isolated component, but it is instead a part of a system resulting in an action. Thus, in robotics the vision research must include consideration of the control of the system, in other words, the entire perception-action loop. This requires that a holistic system approach is useful and could provide significant advances in this application domain. Assembled here is a collection of some of the state of the art methods that are using computer vision and machine learning techniques and apply them in robotic applications.

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